TY - JOUR A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Multi-object Segmentation with Coupled Deformable Models JF - Annals of the British Machine Vision Association (BMVA) Y1 - 2009 VL - 5 SP - 1 EP - 10 ER - TY - JOUR A1 - Zachow, Stefan A1 - Muigg, Philipp A1 - Hildebrandt, Thomas A1 - Doleisch, Helmut A1 - Hege, Hans-Christian T1 - Visual Exploration of Nasal Airflow JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2009 U6 - https://doi.org/10.1109/TVCG.2009.198 VL - 15 IS - 8 SP - 1407 EP - 1414 ER - TY - JOUR A1 - Zachow, Stefan A1 - Deuflhard, Peter T1 - Computergestützte Planung in der kraniofazialen Chirurgie JF - Face 01/08, Int. Mag. of Orofacial Esthetics Y1 - 2008 SP - 43 EP - 49 PB - Oemus Journale Leipzig ER - TY - CHAP A1 - Zilske, Michael A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Adaptive Remeshing of Non-Manifold Surfaces T2 - Eurographics 2008 Annex to the Conf. Proc. Y1 - 2008 SP - 207 EP - 211 ER - TY - CHAP A1 - Seim, Heiko A1 - Lamecker, Hans A1 - Zachow, Stefan T1 - Segmentation of Bony Structures with Ligament Attachment Sites T2 - Bildverarbeitung für die Medizin 2008 Y1 - 2008 U6 - https://doi.org/10.1007/978-3-540-78640-5_42 SP - 207 EP - 211 PB - Springer ER - TY - CHAP A1 - Neugebauer, Mathias A1 - Janiga, Gabor A1 - Zachow, Stefan A1 - Krischek, Özlem A1 - Preim, Bernhard ED - Hauser, Helwig T1 - Generierung qualitativ hochwertiger Modelle für die Simulation von Blutfluss in zerebralen Aneurysmen T2 - Proc. of Simulation and Visualization 2008 Y1 - 2008 SP - 221 EP - 235 ER - TY - CHAP A1 - Dornheim, Jana A1 - Born, Silvia A1 - Zachow, Stefan A1 - Gessat, Michael A1 - Wellein, Daniela A1 - Strauß, Gero A1 - Preim, Bernhard A1 - Bartz, Dirk ED - Hauser, Helwig T1 - Bildanalyse, Visualisierung und Modellerstellung für die Implantatplanung im Mittelohr T2 - Proc. of Simulation and Visualization 2008 Y1 - 2008 SP - 139 EP - 154 ER - TY - JOUR A1 - Steinmann, Alexander A1 - Bartsch, Peter A1 - Zachow, Stefan A1 - Hildebrandt, Thomas T1 - Breathing Easily: Simulation of airflow in human noses can become a useful rhinosurgery planning tool JF - ANSYS Advantage Y1 - 2008 VL - Vol. II, No. 1 SP - 30 EP - 31 ER - TY - JOUR A1 - Weiser, Martin A1 - Zachow, Stefan A1 - Deuflhard, Peter T1 - Craniofacial Surgery Planning Based on Virtual Patient Models JF - it - Information Technology Y1 - 2010 U6 - https://doi.org/10.1524/itit.2010.0600 VL - 52 IS - 5 SP - 258 EP - 263 PB - Oldenbourg Verlagsgruppe ER - TY - CHAP A1 - Zachow, Stefan A1 - Hahn, Horst A1 - Lange, Thomas ED - Schlag, Peter ED - Eulenstein, Sebastian ED - Lange, Thomas T1 - Computerassistierte Chirugieplanung T2 - Computerassistierte Chirurgie Y1 - 2010 SP - 119 EP - 149 PB - Elsevier ER - TY - CHAP A1 - Kainmüller, Dagmar A1 - Lamecker, Hans A1 - Seim, Heiko A1 - Zachow, Stefan A1 - Hege, Hans-Christian ED - Navab, Tianzi ED - P. W. Pluim, Josien ED - Viergever, Max T1 - Improving Deformable Surface Meshes through Omni-directional Displacements and MRFs T2 - Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI) Y1 - 2010 U6 - https://doi.org/10.1007/978-3-642-15705-9_28 VL - 6361 SP - 227 EP - 234 PB - Springer ER - TY - CHAP A1 - Zachow, Stefan A1 - Kubiack, Kim A1 - Malinowski, Jana A1 - Lamecker, Hans A1 - Essig, Harald A1 - Gellrich, Nils-Claudius T1 - Modellgestützte chirurgische Rekonstruktion komplexer Mittelgesichtsfrakturen T2 - Proc. BMT, Biomed Tech 2010 Y1 - 2010 VL - 55 (Suppl 1) SP - 107 EP - 108 PB - Walter de Gruyter-Verlag ER - TY - CHAP A1 - Lamecker, Hans A1 - Kainmüller, Dagmar A1 - Seim, Heiko A1 - Zachow, Stefan T1 - Automatische 3D Rekonstruktion des Unterkiefers und der Mandibulärnerven auf Basis dentaler Bildgebung T2 - Proc. BMT, Biomed Tech Y1 - 2010 VL - 55 (Suppl. 1) SP - 35 EP - 36 PB - Walter de Gruyter-Verlag ER - TY - JOUR A1 - Dworzak, Jalda A1 - Lamecker, Hans A1 - von Berg, Jens A1 - Klinder, Tobias A1 - Lorenz, Cristian A1 - Kainmüller, Dagmar A1 - Seim, Heiko A1 - Hege, Hans-Christian A1 - Zachow, Stefan T1 - 3D Reconstruction of the Human Rib Cage from 2D Projection Images using a Statistical Shape Model JF - Int. J. Comput. Assist. Radiol. Surg. Y1 - 2010 U6 - https://doi.org/10.1007/s11548-009-0390-2 SN - 1861-6410 VL - 5 IS - 2 SP - 111 EP - 124 PB - Springer ER - TY - GEN A1 - Kamer, Lukas A1 - Noser, Hansrudi A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Wittmers, Antonia A1 - Kaup, Thomas A1 - Schramm, Alexander A1 - Hammer, Beat T1 - Three-dimensional statistical shape analysis - A useful tool for developing a new type of orbital implant? Y1 - 2006 SP - 20 EP - 21 PB - AO Development Institute, New Products Brochure 2/06 ER - TY - JOUR A1 - Heppt, Werner A1 - Hildebrandt, Thomas A1 - Steinmann, Alexander A1 - Zachow, Stefan T1 - Aesthetic and Function in Rhinoplasty JF - Springer Journal Y1 - 2007 VL - 264 (Suppl 1), RL 126 SP - 307 ER - TY - CHAP A1 - Zachow, Stefan A1 - Zilske, Michael A1 - Hege, Hans-Christian T1 - 3D Reconstruction of Individual Anatomy from Medical Image Data: Segmentation and Geometry Processing T2 - 25. ANSYS Conference & CADFEM Users’ Meeting Y1 - 2007 CY - Dresden ER - TY - JOUR A1 - Gessat, Michael A1 - Zachow, Stefan A1 - Burgert, Oliver A1 - Lemke, Heinz T1 - Geometric Meshes in Medical Applications - Steps towards a specification of Geometric Models in DICOM JF - Int. J. of Computer Assisted Radiology and Surgery (CARS) Y1 - 2007 U6 - https://doi.org/10.1007/s11548-007-0112-6 SP - 440 EP - 442 ER - TY - JOUR A1 - Hildebrandt, Thomas A1 - Zachow, Stefan A1 - Steinmann, Alexander A1 - Heppt, Werner T1 - Innovation in der Funktionell-Ästhetischen Nasenchirurgie: Rhino-CFD JF - Face, Int. Mag. of Orofacial Esthetics Y1 - 2007 SP - 20 EP - 23 PB - Oemus Journale Leipzig ER - TY - JOUR A1 - Zachow, Stefan A1 - Steinmann, Alexander A1 - Hildebrandt, Thomas A1 - Heppt, Werner T1 - Understanding nasal airflow via CFD simulation and visualization JF - Proc. Computer Aided Surgery around the Head Y1 - 2007 SP - 173 EP - 176 ER - TY - JOUR A1 - Lamecker, Hans A1 - Kamer, Lukas A1 - Wittmers, Antonia A1 - Zachow, Stefan A1 - Kaup, Thomas A1 - Schramm, Alexander A1 - Noser, Hansrudi A1 - Hammer, Beat T1 - A method for the three-dimensional statistical shape analysis of the bony orbit JF - Proc. Computer Aided Surgery around the Head Y1 - 2007 SP - 94 EP - 97 ER - TY - CHAP A1 - Zachow, Stefan A1 - Lamecker, Hans A1 - Elsholtz, Barbara A1 - Stiller, Michael T1 - Reconstruction of mandibular dysplasia using a statistical 3D shape model T2 - Proc. Computer Assisted Radiology and Surgery (CARS) Y1 - 2005 U6 - https://doi.org/10.1016/j.ics.2005.03.339 SP - 1238 EP - 1243 CY - Berlin, Germany ER - TY - CHAP A1 - Nkenke, Emeka A1 - Häusler, Gerd A1 - Neukam, Friedrich A1 - Zachow, Stefan T1 - Streak artifact correction of CT data by optical 3D imaging in the simulation of orthognathic surgery T2 - Computer Assisted Radiology and Surgery (CARS) Y1 - 2005 U6 - https://doi.org/doi:10.1016/j.ics.2005.03.278 CY - Berlin Germany ER - TY - CHAP A1 - Nkenke, Emeka A1 - Zachow, Stefan A1 - Häusler, Gerd T1 - Fusion von optischen 3D- und CT-Daten des Gebisses zur Metallartefaktkorrektur vor computerassistierter Planung MKG-chirurgischer Eingriffe T2 - Symposium der Arbeitsgemeinschaf für Kieferchirurgie Y1 - 2005 CY - Bad Homburg v.d.H ER - TY - JOUR A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Haberl, Hannes A1 - Stiller, Michael T1 - Medical applications for statistical shape models JF - Computer Aided Surgery around the Head, Fortschritt-Berichte VDI - Biotechnik/Medizintechnik Y1 - 2005 VL - 17 (258) SP - 61 ER - TY - JOUR A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Wittmers, Antonia A1 - Weber, Britta A1 - Hege, Hans-Christian A1 - Elsholtz, Barbara A1 - Stiller, Michael T1 - Automatic segmentation of mandibles in low-dose CT-data JF - Int. J. Computer Assisted Radiology and Surgery Y1 - 2006 VL - 1(1) SP - 393 EP - 395 ER - TY - JOUR A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hege, Hans-Christian A1 - Zöckler, Maja T1 - Surgical treatment of craniosynostosis based on a statistical 3D-shape model JF - Int. J. Computer Assisted Radiology and Surgery Y1 - 2006 U6 - https://doi.org/10.1007/s11548-006-0024-x VL - 1(1) SP - 253 EP - 254 ER - TY - JOUR A1 - Zachow, Stefan A1 - Hege, Hans-Christian A1 - Deuflhard, Peter T1 - Computer assisted planning in cranio-maxillofacial surgery JF - Journal of Computing and Information Technology Y1 - 2006 VL - 14(1) SP - 53 EP - 64 ER - TY - JOUR A1 - Zachow, Stefan A1 - Lamecker, Hans A1 - Elsholtz, Barbara A1 - Stiller, Michael T1 - Is the course of the mandibular nerve deducible from the shape of the mandible? JF - Int. J. of Computer Assisted Radiology and Surgery Y1 - 2006 SP - 415 EP - 417 PB - Springer ER - TY - JOUR A1 - Zachow, Stefan A1 - Steinmann, Alexander A1 - Hildebrandt, Thomas A1 - Weber, Rainer A1 - Heppt, Werner T1 - CFD simulation of nasal airflow: Towards treatment planning for functional rhinosurgery JF - Int. J. of Computer Assisted Radiology and Surgery Y1 - 2006 SP - 165 EP - 167 PB - Springer ER - TY - CHAP A1 - Ehlke, Moritz A1 - Frenzel, Thomas A1 - Ramm, Heiko A1 - Shandiz, Mohsen Akbari A1 - Anglin, Carolyn A1 - Zachow, Stefan T1 - Towards Robust Measurement Of Pelvic Parameters From AP Radiographs Using Articulated 3D Models T2 - Computer Assisted Radiology and Surgery (CARS) N2 - Patient-specific parameters such as the orientation of the acetabulum or pelvic tilt are useful for custom planning for total hip arthroplasty (THA) and for evaluating the outcome of surgical interventions. The gold standard in obtaining pelvic parameters is from three-dimensional (3D) computed tomography (CT) imaging. However, this adds time and cost, exposes the patient to a substantial radiation dose, and does not allow for imaging under load (e.g. while the patient is standing). If pelvic parameters could be reliably derived from the standard anteroposterior (AP) radiograph, preoperative planning would be more widespread, and research analyses could be applied to retrospective data, after a postoperative issue is discovered. The goal of this work is to enable robust measurement of two surgical parameters of interest: the tilt of the anterior pelvic plane (APP) and the orientation of the natural acetabulum. We present a computer-aided reconstruction method to determine the APP and natural acetabular orientation from a single, preoperative X-ray. It can easily be extended to obtain other important preoperative and postoperative parameters solely based on a single AP radiograph. KW - Articulated Models, Statistical Shape And Intensity Models, 2D/3D Anatomy Reconstruction, Pelvic Parameters Measurement, Total Hip Arthroplasty Y1 - 2015 ER - TY - GEN A1 - Ehlke, Moritz A1 - Frenzel, Thomas A1 - Ramm, Heiko A1 - Shandiz, Mohsen Akbari A1 - Anglin, Carolyn A1 - Zachow, Stefan T1 - Towards Robust Measurement of Pelvic Parameters from AP Radiographs using Articulated 3D Models N2 - Patient-specific parameters such as the orientation of the acetabulum or pelvic tilt are useful for custom planning for total hip arthroplasty (THA) and for evaluating the outcome of surgical interventions. The gold standard in obtaining pelvic parameters is from three-dimensional (3D) computed tomography (CT) imaging. However, this adds time and cost, exposes the patient to a substantial radiation dose, and does not allow for imaging under load (e.g. while the patient is standing). If pelvic parameters could be reliably derived from the standard anteroposterior (AP) radiograph, preoperative planning would be more widespread, and research analyses could be applied to retrospective data, after a postoperative issue is discovered. The goal of this work is to enable robust measurement of two surgical parameters of interest: the tilt of the anterior pelvic plane (APP) and the orientation of the natural acetabulum. We present a computer-aided reconstruction method to determine the APP and natural acetabular orientation from a single, preoperative X-ray. It can easily be extended to obtain other important preoperative and postoperative parameters solely based on a single AP radiograph. T3 - ZIB-Report - 15-11 KW - Articulated Models, Statistical Shape And Intensity Models, 2D/3D Anatomy Reconstruction, Pelvic Parameters Measurement, Total Hip Arthroplasty Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-53707 SN - 1438-0064 ER - TY - JOUR A1 - Lemanis, Robert A1 - Zachow, Stefan A1 - Fusseis, Florian A1 - Hoffmann, René T1 - A new approach using high-resolution computed tomography to test the buoyant properties of chambered cephalopod shells JF - Paleobiology N2 - The chambered shell of modern cephalopods functions as a buoyancy apparatus, allowing the animal to enter the water column without expending a large amount of energy to overcome its own weight. Indeed, the chambered shell is largely considered a key adaptation that allowed the earliest cephalopods to leave the ocean floor and enter the water column. It has been argued by some, however, that the iconic chambered shell of Paleozoic and Mesozoic ammonoids did not provide a sufficiently buoyant force to compensate for the weight of the entire animal, thus restricting ammonoids to a largely benthic lifestyle reminiscent of some octopods. Here we develop a technique using high-resolution computed tomography to quantify the buoyant properties of chambered shells without reducing the shell to ideal spirals or eliminating inherent biological variability by using mathematical models that characterize past work in this area. This technique has been tested on Nautilus pompilius and is now extended to the extant deep-sea squid Spirula spirula and the Jurassic ammonite Cadoceras sp. hatchling. Cadoceras is found to have possessed near-neutral to positive buoyancy if hatched when the shell possessed between three and five chambers. However, we show that the animal could also overcome degrees of negative buoyancy through swimming, similar to the paralarvae of modern squids. These calculations challenge past inferences of benthic life habits based solely on calculations of negative buoyancy. The calculated buoyancy of Cadoceras supports the possibility of planktonic dispersal of ammonite hatchlings. This information is essential to understanding ammonoid ecology as well as biotic interactions and has implications for the interpretation of geochemical data gained from the isotopic analysis of the shell. Y1 - 2015 U6 - https://doi.org/10.1017/pab.2014.17 VL - 41 IS - 2 SP - 313 EP - 329 PB - Cambridge University Press CY - Cambridge ER - TY - CHAP A1 - Tack, Alexander A1 - Zachow, Stefan T1 - Accurate Automated Volumetry of Cartilage of the Knee using Convolutional Neural Networks: Data from the Osteoarthritis Initiative T2 - IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) N2 - Volumetry of cartilage of the knee is needed for knee osteoarthritis (KOA) assessment. It is typically performed manually in a tedious and subjective process. We developed a method for an automated, segmentation-based quantification of cartilage volume by employing 3D Convolutional Neural Networks (CNNs). CNNs were trained in a supervised manner using magnetic resonance imaging data and cartilage volumetry readings performed by clinical experts for 1378 subjects provided by the Osteoarthritis Initiative. It was shown that 3D CNNs are able to achieve volume measures comparable to the magnitude of variation between expert readings and the real in vivo situation. In the future, accurate automated cartilage volumetry might support both, diagnosis of KOA as well as longitudinal analysis of KOA progression. Y1 - 2019 U6 - https://doi.org/10.1109/ISBI.2019.8759201 SP - 40 EP - 43 ER - TY - CHAP A1 - Neumann, Mario A1 - Hellwich, Olaf A1 - Zachow, Stefan T1 - Localization and Classification of Teeth in Cone Beam CT using Convolutional Neural Networks T2 - Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC) N2 - In dentistry, software-based medical image analysis and visualization provide efficient and accurate diagnostic and therapy planning capabilities. We present an approach for the automatic recognition of tooth types and positions in digital volume tomography (DVT). By using deep learning techniques in combination with dimensionality reduction through non-planar reformatting of the jaw anatomy, DVT data can be efficiently processed and teeth reliably recognized and classified, even in the presence of imaging artefacts, missing or dislocated teeth. We evaluated our approach, which is based on 2D Convolutional Neural Networks (CNNs), on 118 manually annotated cases of clinical DVT datasets. Our proposed method correctly classifies teeth with an accuracy of 94% within a limit of 2mm distance to ground truth labels. Y1 - 2019 SN - 978-3-00-063717-9 SP - 182 EP - 188 ER - TY - CHAP A1 - Joachimsky, Robert A1 - Ma, Lihong A1 - Icking, Christian A1 - Zachow, Stefan T1 - A Collision-Aware Articulated Statistical Shape Model of the Human Spine T2 - Proc. of the 18th annual conference on Computer- and Robot-assisted Surgery (CURAC) N2 - Statistical Shape Models (SSMs) are a proven means for model-based 3D anatomy reconstruction from medical image data. In orthopaedics and biomechanics, SSMs are increasingly employed to individualize measurement data or to create individualized anatomical models to which implants can be adapted to or functional tests can be performed on. For modeling and analysis of articulated structures, so called articulated SSMs (aSSMs) have been developed. However, a missing feature of aSSMs is the consideration of collisions in the course of individual fitting and articulation. The aim of our work was to develop aSSMs that handle collisions between components correctly. That way it becomes possible to adjust shape and articulation in view of a physically and geometrically plausible individualization. To be able to apply collision-aware aSSMs in simulation and optimisation, our approach is based on an e� cient collision detection method employing Graphics Processing Units (GPUs). Y1 - 2019 SP - 58 EP - 64 ER - TY - JOUR A1 - Krämer, Martin A1 - Maggioni, Marta A1 - Brisson, Nicholas A1 - Zachow, Stefan A1 - Teichgräber, Ulf A1 - Duda, Georg A1 - Reichenbach, Jürgen T1 - T1 and T2* mapping of the human quadriceps and patellar tendons using ultra-short echo-time (UTE) imaging and bivariate relaxation parameter-based volumetric visualization JF - Magnetic Resonance Imaging N2 - Quantification of magnetic resonance (MR)-based relaxation parameters of tendons and ligaments is challenging due to their very short transverse relaxation times, requiring application of ultra-short echo-time (UTE) imaging sequences. We quantify both T1 and T2⁎ in the quadriceps and patellar tendons of healthy volunteers at a field strength of 3 T and visualize the results based on 3D segmentation by using bivariate histogram analysis. We applied a 3D ultra-short echo-time imaging sequence with either variable repetition times (VTR) or variable flip angles (VFA) for T1 quantification in combination with multi-echo acquisition for extracting T2⁎. The values of both relaxation parameters were subsequently binned for bivariate histogram analysis and corresponding cluster identification, which were subsequently visualized. Based on manually-drawn regions of interest in the tendons on the relaxation parameter maps, T1 and T2⁎ boundaries were selected in the bivariate histogram to segment the quadriceps and patellar tendons and visualize the relaxation times by 3D volumetric rendering. Segmentation of bone marrow, fat, muscle and tendons was successfully performed based on the bivariate histogram analysis. Based on the segmentation results mean T2⁎ relaxation times, over the entire tendon volumes averaged over all subjects, were 1.8 ms ± 0.1 ms and 1.4 ms ± 0.2 ms for the patellar and quadriceps tendons, respectively. The mean T1 value of the patellar tendon, averaged over all subjects, was 527 ms ± 42 ms and 476 ms ± 40 ms for the VFA and VTR acquisitions, respectively. The quadriceps tendon had higher mean T1 values of 662 ms ± 97 ms (VFA method) and 637 ms ± 40 ms (VTR method) compared to the patellar tendon. 3D volumetric visualization of the relaxation times revealed that T1 values are not constant over the volume of both tendons, but vary locally. This work provided additional data to build upon the scarce literature available on relaxation times in the quadriceps and patellar tendons. We were able to segment both tendons and to visualize the relaxation parameter distributions over the entire tendon volumes. Y1 - 2019 U6 - https://doi.org/10.1016/j.mri.2019.07.015 VL - 63 IS - 11 SP - 29 EP - 36 ER - TY - CHAP A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - An as-invariant-as-possible GL+(3)-based Statistical Shape Model T2 - Proc. 7th MICCAI workshop on Mathematical Foundations of Computational Anatomy (MFCA) N2 - We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling. Y1 - 2019 U6 - https://doi.org/10.1007/978-3-030-33226-6_23 VL - 11846 SP - 219 EP - 228 PB - Springer ER - TY - GEN A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - A Surface-Theoretic Approach for Statistical Shape Modeling N2 - We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. We evaluate the performance of our model w.r.t. shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing natural biological shape variability, we carry out an analysis of specificity and generalization ability. T3 - ZIB-Report - 19-20 KW - Statistical shape analysis KW - Principal geodesic analysis KW - Lie groups KW - Classification KW - Manifold valued statistics Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-74497 SN - 1438-0064 ER - TY - GEN A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - An as-invariant-as-possible GL+(3)-based Statistical Shape Model N2 - We describe a novel nonlinear statistical shape model basedon differential coordinates viewed as elements of GL+(3). We adopt an as-invariant-as possible framework comprising a bi-invariant Lie group mean and a tangent principal component analysis based on a unique GL+(3)-left-invariant, O(3)-right-invariant metric. Contrary to earlier work that equips the coordinates with a specifically constructed group structure, our method employs the inherent geometric structure of the group-valued data and therefore features an improved statistical power in identifying shape differences. We demonstrate this in experiments on two anatomical datasets including comparison to the standard Euclidean as well as recent state-of-the-art nonlinear approaches to statistical shape modeling. T3 - ZIB-Report - 19-46 KW - Statistical shape analysis KW - Tangent principal component analysis KW - Lie groups KW - Classification KW - Manifold valued statistics Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-74566 SN - 1438-0064 ER - TY - CHAP A1 - Sahu, Manish A1 - Strömsdörfer, Ronja A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation T2 - Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI), Part III N2 - Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems. Recent success of image-based solutions using fully-supervised deep learning approaches can be attributed to the collection of big labeled datasets. However, the annotation of a big dataset of real videos can be prohibitively expensive and time consuming. Computer simulations could alleviate the manual labeling problem, however, models trained on simulated data do not generalize to real data. This work proposes a consistency-based framework for joint learning of simulated and real (unlabeled) endoscopic data to bridge this performance generalization issue. Empirical results on two data sets (15 videos of the Cholec80 and EndoVis'15 dataset) highlight the effectiveness of the proposed Endo-Sim2Real method for instrument segmentation. We compare the segmentation of the proposed approach with state-of-the-art solutions and show that our method improves segmentation both in terms of quality and quantity. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1007/978-3-030-59716-0_75 VL - 12263 PB - Springer Nature ER - TY - JOUR A1 - Sahu, Manish A1 - Szengel, Angelika A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Surgical phase recognition by learning phase transitions JF - Current Directions in Biomedical Engineering (CDBME) N2 - Automatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1% on overall precision for phase detection tasks and 12.5% on meanAP for tool recognition tasks. Y1 - 2020 U6 - https://doi.org/https://doi.org/10.1515/cdbme-2020-0037 N1 - Nomination for the Best-Paper Award VL - 6 IS - 1 SP - 20200037 PB - De Gruyter ER - TY - GEN A1 - Sahu, Manish A1 - Szengel, Angelika A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Analyzing laparoscopic cholecystectomy with deep learning: automatic detection of surgical tools and phases T2 - 28th International Congress of the European Association for Endoscopic Surgery (EAES) N2 - Motivation: The ever-rising volume of patients, high maintenance cost of operating rooms and time consuming analysis of surgical skills are fundamental problems that hamper the practical training of the next generation of surgeons. The hospitals prefer to keep the surgeons busy in real operations over training young surgeons for obvious economic reasons. One fundamental need in surgical training is the reduction of the time needed by the senior surgeon to review the endoscopic procedures performed by the young surgeon while minimizing the subjective bias in evaluation. The unprecedented performance of deep learning ushers the new age of data-driven automatic analysis of surgical skills. Method: Deep learning is capable of efficiently analyzing thousands of hours of laparoscopic video footage to provide an objective assessment of surgical skills. However, the traditional end-to-end setting of deep learning (video in, skill assessment out) is not explainable. Our strategy is to utilize the surgical process modeling framework to divide the surgical process into understandable components. This provides the opportunity to employ deep learning for superior yet automatic detection and evaluation of several aspects of laparoscopic cholecystectomy such as surgical tool and phase detection. We employ ZIBNet for the detection of surgical tool presence. ZIBNet employs pre-processing based on tool usage imbalance, a transfer learned 50-layer residual network (ResNet-50) and temporal smoothing. To encode the temporal evolution of tool usage (over the entire video sequence) that relates to the surgical phases, Long Short Term Memory (LSTM) units are employed with long-term dependency. Dataset: We used CHOLEC 80 dataset that consists of 80 videos of laparoscopic cholecystectomy performed by 13 surgeons, divided equally for training and testing. In these videos, up to three different tools (among 7 types of tools) can be present in a frame. Results: The mean average precision of the detection of all tools is 93.5 ranging between 86.8 and 99.3, a significant improvement (p <0.01) over the previous state-of-the-art. We observed that less frequent tools like Scissors, Irrigator, Specimen Bag etc. are more related to phase transitions. The overall precision (recall) of the detection of all surgical phases is 79.6 (81.3). Conclusion: While this is not the end goal for surgical skill analysis, the development of such a technological platform is essential toward a data-driven objective understanding of surgical skills. In future, we plan to investigate surgeon-in-the-loop analysis and feedback for surgical skill analysis. Y1 - 2020 UR - https://academy.eaes.eu/eaes/2020/28th/298882/manish.sahu.analyzing.laparoscopic.cholecystectomy.with.deep.learning.html?f=listing%3D0%2Abrowseby%3D8%2Asortby%3D2 ER - TY - JOUR A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms JF - Medical Image Analysis N2 - We present a novel approach for nonlinear statistical shape modeling that is invariant under Euclidean motion and thus alignment-free. By analyzing metric distortion and curvature of shapes as elements of Lie groups in a consistent Riemannian setting, we construct a framework that reliably handles large deformations. Due to the explicit character of Lie group operations, our non-Euclidean method is very efficient allowing for fast and numerically robust processing. This facilitates Riemannian analysis of large shape populations accessible through longitudinal and multi-site imaging studies providing increased statistical power. Additionally, as planar configurations form a submanifold in shape space, our representation allows for effective estimation of quasi-isometric surfaces flattenings. We evaluate the performance of our model w.r.t. shape-based classification of hippocampus and femur malformations due to Alzheimer's disease and osteoarthritis, respectively. In particular, we achieve state-of-the-art accuracies outperforming the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data. To provide insight into the model's ability of capturing biological shape variability, we carry out an analysis of specificity and generalization ability. Y1 - 2021 U6 - https://doi.org/10.1016/j.media.2021.102178 VL - 73 ER - TY - JOUR A1 - Sahu, Manish A1 - Mukhopadhyay, Anirban A1 - Zachow, Stefan T1 - Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation JF - International Journal of Computer Assisted Radiology and Surgery N2 - Purpose Segmentation of surgical instruments in endoscopic video streams is essential for automated surgical scene understanding and process modeling. However, relying on fully supervised deep learning for this task is challenging because manual annotation occupies valuable time of the clinical experts. Methods We introduce a teacher–student learning approach that learns jointly from annotated simulation data and unlabeled real data to tackle the challenges in simulation-to-real unsupervised domain adaptation for endoscopic image segmentation. Results Empirical results on three datasets highlight the effectiveness of the proposed framework over current approaches for the endoscopic instrument segmentation task. Additionally, we provide analysis of major factors affecting the performance on all datasets to highlight the strengths and failure modes of our approach. Conclusions We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art. This takes us one step closer to effective segmentation of surgical instrument in the annotation scarce setting. Y1 - 2021 U6 - https://doi.org/10.1007/s11548-021-02383-4 N1 - Honorary Mention: Machine Learning for Computer-Assisted Intervention (CAI) Award @IPCAI2021 N1 - Honorary Mention: Audience Award for Best Innovation @IPCAI2021 VL - 16 SP - 849 EP - 859 PB - Springer Nature ER - TY - CHAP A1 - Estacio, Laura A1 - Ehlke, Moritz A1 - Tack, Alexander A1 - Castro-Gutierrez, Eveling A1 - Lamecker, Hans A1 - Mora, Rensso A1 - Zachow, Stefan T1 - Unsupervised Detection of Disturbances in 2D Radiographs T2 - 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) N2 - We present a method based on a generative model for detection of disturbances such as prosthesis, screws, zippers, and metals in 2D radiographs. The generative model is trained in an unsupervised fashion using clinical radiographs as well as simulated data, none of which contain disturbances. Our approach employs a latent space consistency loss which has the benefit of identifying similarities, and is enforced to reconstruct X-rays without disturbances. In order to detect images with disturbances, an anomaly score is computed also employing the Frechet distance between the input X-ray and the reconstructed one using our generative model. Validation was performed using clinical pelvis radiographs. We achieved an AUC of 0.77 and 0.83 with clinical and synthetic data, respectively. The results demonstrated a good accuracy of our method for detecting outliers as well as the advantage of utilizing synthetic data. Y1 - 2021 U6 - https://doi.org/10.1109/ISBI48211.2021.9434091 SP - 367 EP - 370 ER - TY - GEN A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Geodesic B-Score for Improved Assessment of Knee Osteoarthritis N2 - Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions. T3 - ZIB-Report - 21-09 KW - Statistical shape analysis KW - Osteoarthritis KW - Geometric statistics KW - Riemannian manifolds Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-81930 SN - 1438-0064 ER - TY - JOUR A1 - Tack, Alexander A1 - Preim, Bernhard A1 - Zachow, Stefan T1 - Fully automated Assessment of Knee Alignment from Full-Leg X-Rays employing a "YOLOv4 And Resnet Landmark regression Algorithm" (YARLA): Data from the Osteoarthritis Initiative JF - Computer Methods and Programs in Biomedicine N2 - We present a method for the quantification of knee alignment from full-leg X-Rays. A state-of-the-art object detector, YOLOv4, was trained to locate regions of interests (ROIs) in full-leg X-Ray images for the hip joint, the knee, and the ankle. Residual neural networks (ResNets) were trained to regress landmark coordinates for each ROI.Based on the detected landmarks the knee alignment, i.e., the hip-knee-ankle (HKA) angle, was computed. The accuracy of landmark detection was evaluated by a comparison to manually placed landmarks for 360 legs in 180 X-Rays. The accuracy of HKA angle computations was assessed on the basis of 2,943 X-Rays. Results of YARLA were compared to the results of two independent image reading studies(Cooke; Duryea) both publicly accessible via the Osteoarthritis Initiative. The agreement was evaluated using Spearman's Rho, and weighted kappa as well as regarding the correspondence of the class assignment (varus/neutral/valgus). The average difference between YARLA and manually placed landmarks was less than 2.0+- 1.5 mm for all structures (hip, knee, ankle). The average mismatch between HKA angle determinations of Cooke and Duryea was 0.09 +- 0.63°; YARLA resulted in a mismatch of 0.10 +- 0.74° compared to Cooke and of 0.18 +- 0.64° compared to Duryea. Cooke and Duryea agreed almost perfectly with respect to a weighted kappa value of 0.86, and showed an excellent reliability as measured by a Spearman's Rho value of 0.99. Similar values were achieved by YARLA, i.e., a weighted kappa value of0.83 and 0.87 and a Spearman's Rho value of 0.98 and 0.99 to Cooke and Duryea,respectively. Cooke and Duryea agreed in 92% of all class assignments and YARLA did so in 90% against Cooke and 92% against Duryea. In conclusion, YARLA achieved results comparable to those of human experts and thus provides a basis for an automated assessment of knee alignment in full-leg X-Rays. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1016/j.cmpb.2021.106080 VL - 205 IS - 106080 ER - TY - CHAP A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - von Tycowicz, Christoph T1 - Geodesic B-Score for Improved Assessment of Knee Osteoarthritis T2 - Proc. Information Processing in Medical Imaging (IPMI) N2 - Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions. Y1 - 2021 U6 - https://doi.org/10.1007/978-3-030-78191-0_14 SP - 177 EP - 188 ER - TY - JOUR A1 - Hembus, Jessica A1 - Ambellan, Felix A1 - Zachow, Stefan A1 - Bader, Rainer T1 - Establishment of a rolling-sliding test bench to analyze abrasive wear propagation of different bearing materials for knee implants JF - Applied Sciences N2 - Currently, new materials for knee implants need to be extensively and expensive tested in a knee wear simulator in a realized design. However, using a rolling-sliding test bench, these materials can be examined under the same test conditions but with simplified geometries. In the present study, the test bench was optimized, and forces were adapted to the physiological contact pressure in the knee joint using the available geometric parameters. Various polymers made of polyethylene and polyurethane articulating against test wheels made of cobalt-chromium and aluminum titanate were tested in the test bench using adapted forces based on ISO 14243-1. Polyurethane materials showed distinctly higher wear rates than polyethylene materials and showed inadequate wear resistance for use as knee implant material. Thus, the rolling-sliding test bench is an adaptable test setup for evaluating newly developed bearing materials for knee implants. It combines the advantages of screening and simulator tests and allows testing of various bearing materials under physiological load and tribological conditions of the human knee joint. The wear behavior of different material compositions and the influence of surface geometry and quality can be initially investigated without the need to produce complex implant prototypes of total knee endoprosthesis or interpositional spacers. Y1 - 2021 U6 - https://doi.org/10.3390/app11041886 VL - 11 IS - 4 ER -